{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T11:51:17Z","timestamp":1785498677923,"version":"3.56.0"},"reference-count":60,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T00:00:00Z","timestamp":1720656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institutes of Health (NIH)","award":["DK076648"],"award-info":[{"award-number":["DK076648"]}]},{"name":"National Institutes of Health (NIH)","award":["DK068001"],"award-info":[{"award-number":["DK068001"]}]},{"name":"National Institutes of Health (NIH)","award":["HD108272"],"award-info":[{"award-number":["HD108272"]}]},{"name":"National Institutes of Health (NIH)","award":["HL146166-04S1"],"award-info":[{"award-number":["HL146166-04S1"]}]},{"name":"National Institutes of Health (NIH)","award":["ES005022"],"award-info":[{"award-number":["ES005022"]}]},{"name":"National Institutes of Health (NIH)","award":["U01CA235507"],"award-info":[{"award-number":["U01CA235507"]}]},{"name":"National Institutes of Health (NIH)","award":["U2CES030857"],"award-info":[{"award-number":["U2CES030857"]}]},{"name":"National Institutes of Health (NIH)","award":["UH3OD023248"],"award-info":[{"award-number":["UH3OD023248"]}]},{"name":"National Institutes of Health (NIH)","award":["U24CA268153"],"award-info":[{"award-number":["U24CA268153"]}]},{"name":"National Institutes of Health (NIH)","award":["1262416"],"award-info":[{"award-number":["1262416"]}]},{"name":"National Science Foundation (NSF)","award":["DK076648"],"award-info":[{"award-number":["DK076648"]}]},{"name":"National Science Foundation (NSF)","award":["DK068001"],"award-info":[{"award-number":["DK068001"]}]},{"name":"National Science Foundation (NSF)","award":["HD108272"],"award-info":[{"award-number":["HD108272"]}]},{"name":"National Science Foundation (NSF)","award":["HL146166-04S1"],"award-info":[{"award-number":["HL146166-04S1"]}]},{"name":"National Science Foundation (NSF)","award":["ES005022"],"award-info":[{"award-number":["ES005022"]}]},{"name":"National Science Foundation (NSF)","award":["U01CA235507"],"award-info":[{"award-number":["U01CA235507"]}]},{"name":"National Science Foundation (NSF)","award":["U2CES030857"],"award-info":[{"award-number":["U2CES030857"]}]},{"name":"National Science Foundation (NSF)","award":["UH3OD023248"],"award-info":[{"award-number":["UH3OD023248"]}]},{"name":"National Science Foundation (NSF)","award":["U24CA268153"],"award-info":[{"award-number":["U24CA268153"]}]},{"name":"National Science Foundation (NSF)","award":["1262416"],"award-info":[{"award-number":["1262416"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Maternal metabolism during pregnancy shapes offspring health via in utero programming. In the Healthy Start study, we identified five subgroups of pregnant women based on conventional metabolic biomarkers: Reference (n = 360); High HDL-C (n = 289); Dyslipidemic\u2013High TG (n = 149); Dyslipidemic\u2013High FFA (n = 180); Insulin Resistant (IR)\u2013Hyperglycemic (n = 87). These subgroups not only captured metabolic heterogeneity among pregnant participants but were also associated with offspring obesity in early childhood, even among women without obesity or diabetes. Here, we utilize metabolomics data to enrich characterization of the metabolic subgroups and identify key compounds driving between-group differences. We analyzed fasting blood samples from 1065 pregnant women at 18 gestational weeks using untargeted metabolomics. We used weighted gene correlation network analysis (WGCNA) to derive a global network based on the Reference subgroup and characterized distinct metabolite modules representative of the different metabolomic profiles. We used the mummichog algorithm for pathway enrichment and identified key compounds that differed across the subgroups. Eight metabolite modules representing pathways such as the carnitine\u2013acylcarnitine translocase system, fatty acid biosynthesis and activation, and glycerophospholipid metabolism were identified. A module that included 189 compounds related to DHA peroxidation, oxidative stress, and sex hormone biosynthesis was elevated in the Insulin Resistant\u2013Hyperglycemic vs. the Reference subgroup. This module was positively correlated with total cholesterol (R:0.10; p-value &lt; 0.0001) and free fatty acids (R:0.07; p-value &lt; 0.05). Oxidative stress and inflammatory pathways may underlie insulin resistance during pregnancy, even below clinical diabetes thresholds. These findings highlight potential therapeutic targets and strategies for pregnancy risk stratification and reveal mechanisms underlying the developmental origins of metabolic disease risk.<\/jats:p>","DOI":"10.3390\/ijms25147620","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T08:47:41Z","timestamp":1720774061000},"page":"7620","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Maternal Serum Metabolomics in Mid-Pregnancy Identifies Lipid Pathways as a Key Link to Offspring Obesity in Early Childhood"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8618-045X","authenticated-orcid":false,"given":"Ellen C.","family":"Francis","sequence":"first","affiliation":[{"name":"Department of Biostatistics & Epidemiology, Rutgers School of Public Health, Piscataway, NJ 08854, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3725-5459","authenticated-orcid":false,"given":"Katerina","family":"Kechris","sequence":"additional","affiliation":[{"name":"Department of Biostatistics & Informatics, Colorado School of Public Health, Aurora, CO 80045, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9345-4439","authenticated-orcid":false,"given":"Randi K.","family":"Johnson","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA"},{"name":"Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2587-4022","authenticated-orcid":false,"given":"Shristi","family":"Rawal","sequence":"additional","affiliation":[{"name":"Department of Clinical and Preventive Nutrition Sciences, School of Health Professions, Rutgers University, Newark, NJ 07102, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5199-5744","authenticated-orcid":false,"given":"Wimal","family":"Pathmasiri","sequence":"additional","affiliation":[{"name":"Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA"},{"name":"Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8662-3270","authenticated-orcid":false,"given":"Blake R.","family":"Rushing","sequence":"additional","affiliation":[{"name":"Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA"},{"name":"Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuxia","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Jansson","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dana","family":"Dabelea","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA"},{"name":"The Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan J.","family":"Sumner","sequence":"additional","affiliation":[{"name":"Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA"},{"name":"Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8552-6850","authenticated-orcid":false,"given":"Wei","family":"Perng","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA"},{"name":"The Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Francis, E.C., Kechris, K., Cohen, C.C., Michelotti, G., Dabelea, D., and Perng, W. (2022). Metabolomic Profiles in Childhood and Adolescence Are Associated with Fetal Overnutrition. Metabolites, 12.","DOI":"10.3390\/metabo12030265"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.2337\/dc15-2672","article-title":"Heterogeneous Contribution of Insulin Sensitivity and Secretion Defects to Gestational Diabetes Mellitus","volume":"39","author":"Powe","year":"2016","journal-title":"Diabetes Care"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2064","DOI":"10.2337\/dbi20-0004","article-title":"Defining Heterogeneity Among Women with Gestational Diabetes Mellitus","volume":"69","author":"Powe","year":"2020","journal-title":"Diabetes"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"White, S.L., Begum, S.I., Vieira, M.C., Seed, P., Lawlor, D.L., Sattar, N., Nelson, S.M., Welsh, P., Pasupathy, D., and Poston, L. (2020). Metabolic phenotyping by treatment modality in obese women with gestational diabetes suggests diverse pathophysiology: An exploratory study. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0230658"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.1007\/s00125-017-4380-6","article-title":"Metabolic profiling of gestational diabetes in obese women during pregnancy","volume":"60","author":"White","year":"2017","journal-title":"Diabetologia"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1042\/CS20190266","article-title":"Placental function in maternal obesity","volume":"134","author":"Kelly","year":"2020","journal-title":"Clin. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1038\/s43856-023-00393-8","article-title":"Refining the diagnosis of gestational diabetes mellitus: A systematic review and meta-analysis","volume":"3","author":"Francis","year":"2023","journal-title":"Commun. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1007\/s00125-019-4914-1","article-title":"Developmental overnutrition and obesity and type 2 diabetes in offspring","volume":"62","author":"Perng","year":"2019","journal-title":"Diabetologia"},{"key":"ref_9","unstructured":"Driscoll, A., and Gregory, E. (2020). Increases in Prepregnancy Obesity: United States, 2016\u20132019."},{"key":"ref_10","first-page":"1","article-title":"Trends and Characteristics in Gestational Diabetes: United States, 2016\u20132020","volume":"71","author":"Gregory","year":"2022","journal-title":"Natl. Vital Stat. Rep."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zeitler, P.S., and Nadeau, K.J. (2020). Insulin Resistance in Pregnancy: Implications for Mother and Offspring. Insulin Resistance: Childhood Precursors of Adult Disease, Springer International Publishing.","DOI":"10.1007\/978-3-030-25057-7"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s11892-018-1008-2","article-title":"Maternal Non-glycemic Contributors to Fetal Growth in Obesity and Gestational Diabetes: Spotlight on Lipids","volume":"18","author":"Barbour","year":"2018","journal-title":"Curr. Diabetes Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"S112","DOI":"10.2337\/dc07-s202","article-title":"Cellular Mechanisms for Insulin Resistance in Normal Pregnancy and Gestational Diabetes","volume":"30","author":"Barbour","year":"2007","journal-title":"Diabetes Care"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"302","DOI":"10.3945\/ajcn.114.094946","article-title":"Associations of maternal BMI and gestational weight gain with neonatal adiposity in the Healthy Start study","volume":"101","author":"Starling","year":"2015","journal-title":"Am. J. Clin. Nutr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e237030","DOI":"10.1001\/jamanetworkopen.2023.7030","article-title":"Novel Metabolic Subtypes in Pregnant Women and Risk of Early Childhood Obesity in Offspring","volume":"6","author":"Francis","year":"2023","journal-title":"JAMA Netw. Open"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"780","DOI":"10.2337\/dc11-1790","article-title":"The Hyperglycemia and Adverse Pregnancy Outcome Study: Associations of GDM and obesity with pregnancy outcomes","volume":"35","author":"Catalano","year":"2012","journal-title":"Diabetes Care"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1017\/S204017441500001X","article-title":"Metabolomics in the developmental origins of obesity and its cardiometabolic consequences","volume":"6","author":"Hivert","year":"2015","journal-title":"J. Dev. Orig. Health Dis."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1161\/HYPERTENSIONAHA.122.20807","article-title":"Transcriptomics-Based Subphenotyping of the Human Placenta Enabled by Weighted Correlation Network Analysis in Early-Onset Preeclampsia with and without Fetal Growth Restriction","volume":"80","author":"Ackerman","year":"2023","journal-title":"Hypertension"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"giac004","DOI":"10.1093\/gigascience\/giac004","article-title":"Maternal plasma lipids are involved in the pathogenesis of preterm birth","volume":"11","author":"Chen","year":"2022","journal-title":"Gigascience"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1038\/s41366-021-00750-4","article-title":"A prospective study of maternal adiposity and glycemic traits across pregnancy and mid-childhood metabolomic profiles","volume":"45","author":"Rahman","year":"2021","journal-title":"Int. J. Obes."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.2337\/db15-1748","article-title":"Metabolic Networks and Metabolites Underlie Associations Between Maternal Glucose During Pregnancy and Newborn Size at Birth","volume":"65","author":"Scholtens","year":"2016","journal-title":"Diabetes"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.biochi.2022.12.002","article-title":"LC-MS\/MS based untargeted lipidomics uncovers lipid signatures of late-onset preeclampsia","volume":"208","author":"Yang","year":"2023","journal-title":"Biochimie"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, L., Bi, S., Liang, Y., Huang, L., Li, Y., Huang, M., Huang, B., Deng, W., Liang, J., and Gu, S. (2022). Integrated Metabolomic and Lipidomic Analysis in the Placenta of Preeclampsia. Front. Physiol., 13.","DOI":"10.3389\/fphys.2022.807583"},{"key":"ref_24","unstructured":"Institute of Medicine (2009). Weight Gain during Pregnancy: Reexamining the Guidelines, National Academies Press."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2735","DOI":"10.1161\/CIRCULATIONAHA.105.169404","article-title":"Diagnosis and Management of the Metabolic Syndrome","volume":"112","author":"Grundy","year":"2005","journal-title":"Circulation"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lee, C.H., Shih, A.Z.L., Woo, Y.C., Fong, C.H.Y., Leung, O.Y., Janus, E., Cheung, B.M.Y., and Lam, K.S.L. (2016). Optimal Cut-Offs of Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) to Identify Dysglycemia and Type 2 Diabetes Mellitus: A 15-Year Prospective Study in Chinese. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0163424"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Melmed, S., Auchus, R.J., Goldfine, A.B., Koenig, R., Rosen, C.J., and Williams, R.H. (2020). Williams textbook of endocrinology. Textbook of Endocrinology, Elsevier. [14th ed.].","DOI":"10.33029\/9704-4951-6-PEND-1-848"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1007\/s00592-015-0730-3","article-title":"Cut-off points of homeostasis model assessment of insulin resistance, beta-cell function, and fasting serum insulin to identify future type 2 diabetes: Tehran Lipid and Glucose Study","volume":"52","author":"Ghasemi","year":"2015","journal-title":"Acta Diabetol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1016\/j.amjcard.2012.02.016","article-title":"Relation Among the Plasma Triglyceride\/High-Density Lipoprotein Cholesterol Concentration Ratio, Insulin Resistance, and Associated Cardio-Metabolic Risk Factors in Men and Women","volume":"109","author":"Salazar","year":"2012","journal-title":"Am. J. Cardiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1002\/oby.22246","article-title":"Postprandial Triglycerides Predict Newborn Fat More Strongly than Glucose in Women with Obesity in Early Pregnancy","volume":"26","author":"Barbour","year":"2018","journal-title":"Obesity"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"18S","DOI":"10.1177\/0148607115595980","article-title":"Functional Roles of Fatty Acids and Their Effects on Human Health","volume":"39","author":"Calder","year":"2015","journal-title":"J. Parenter. Enter. Nutr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.clnesp.2024.02.006","article-title":"Bioactive metabolites of OMEGA-6 and OMEGA-3 fatty acids are associated with inflammatory cytokine concentrations in maternal and infant plasma at the time of delivery","volume":"60","author":"Slotkowski","year":"2024","journal-title":"Clin. Nutr. ESPEN"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"151461","DOI":"10.1016\/j.acthis.2019.151461","article-title":"Prophylactic supplementation of 20-HETE ameliorates hypoxia\/reoxygenation injury in pulmonary vascular endothelial cells by inhibiting apoptosis","volume":"122","author":"Sugumaran","year":"2020","journal-title":"Acta Histochem."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/bs.apha.2015.03.003","article-title":"Chapter Two\u2014Involvement of Cytochrome P450 in Reactive Oxygen Species Formation and Cancer","volume":"Volume 74","author":"Hardwick","year":"2015","journal-title":"Advances in Pharmacology"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"13073","DOI":"10.18632\/oncotarget.14375","article-title":"Maternal butyrate supplementation induces insulin resistance associated with enhanced intramuscular fat deposition in the offspring","volume":"8","author":"Huang","year":"2017","journal-title":"Oncotarget"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.biopha.2010.09.017","article-title":"Butyrate, an HDAC inhibitor, stimulates interplay between different posttranslational modifications of histone H3 and differently alters G1-specific cell cycle proteins in vascular smooth muscle cells","volume":"64","author":"Mathew","year":"2010","journal-title":"Biomed Pharmacother"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.immuni.2018.07.017","article-title":"Interferons and Proinflammatory Cytokines in Pregnancy and Fetal Development","volume":"49","author":"Yockey","year":"2018","journal-title":"Immunity"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1089\/ars.2011.4214","article-title":"Is there a role for isofurans and neuroprostanes in pre-eclampsia and normal pregnancy?","volume":"16","author":"Barden","year":"2012","journal-title":"Antioxid Redox Signal"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1002\/oby.21159","article-title":"Maternal obesity increases oxidative stress in the newborn","volume":"23","author":"Gallardo","year":"2015","journal-title":"Obesity"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1017\/S204017441300055X","article-title":"The effects of prenatal oxidative stress levels on infant adiposity development during the first year of life","volume":"5","author":"Loy","year":"2014","journal-title":"J. Dev. Orig. Health Dis."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.placenta.2014.03.010","article-title":"Oxidative stress and maternal obesity: Feto-placental unit interaction","volume":"35","author":"Malti","year":"2014","journal-title":"Placenta"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.1126\/science.294.5548.1871","article-title":"Prostaglandins and leukotrienes: Advances in eicosanoid biology","volume":"294","author":"Funk","year":"2001","journal-title":"Science"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Welch, B.M., Keil, A.P., van \u2018t Erve, T.J., Deterding, L.J., Williams, J.G., Lih, F.B., Cantonwine, D.E., McElrath, T.F., and Ferguson, K.K. (2020). Longitudinal profiles of plasma eicosanoids during pregnancy and size for gestational age at delivery: A nested case-control study. PLoS Med., 17.","DOI":"10.1371\/journal.pmed.1003271"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1038\/nature04634","article-title":"Reactive oxygen species have a causal role in multiple forms of insulin resistance","volume":"440","author":"Houstis","year":"2006","journal-title":"Nature"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Argaev-Frenkel, L., and Rosenzweig, T. (2023). Redox Balance in Type 2 Diabetes: Therapeutic Potential and the Challenge of Antioxidant-Based Therapy. Antioxidants, 12.","DOI":"10.3390\/antiox12050994"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Luo, H., and Zhu, Z. (2023). Serum acylcarnitines levels as a potential predictor for gestational diabetes: A systematic review and meta-analysis. Front. Public Health, 11.","DOI":"10.3389\/fpubh.2023.1217237"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.2337\/dc21-1705","article-title":"Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies","volume":"45","author":"Morze","year":"2022","journal-title":"Diabetes Care"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1161\/01.RES.0000170946.56981.5c","article-title":"New insights into the regulation of HDL metabolism and reverse cholesterol transport","volume":"96","author":"Lewis","year":"2005","journal-title":"Circ. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1002\/oby.20924","article-title":"Exposure to prenatal smoking and early-life body composition: The healthy start study","volume":"23","author":"Harrod","year":"2015","journal-title":"Obesity"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1001\/jama.2021.13304","article-title":"Updated Guidance on the Reporting of Race and Ethnicity in Medical and Science Journals","volume":"326","author":"Flanagin","year":"2021","journal-title":"JAMA"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1210\/jc.2014-2949","article-title":"Maternal fuels and metabolic measures during pregnancy and neonatal body composition: The healthy start study","volume":"100","author":"Crume","year":"2015","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s00125-020-05294-2","article-title":"Maternal blood glucose level and offspring glucose-insulin homeostasis: What is the role of offspring adiposity?","volume":"64","author":"Francis","year":"2021","journal-title":"Diabetologia"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"National Institutes of Health (2013). National Institutes of Health consensus development conference statement: Diagnosing gestational diabetes mellitus, March 4\u20136, 2013. Obstet Gynecol., 122, 358\u2013369.","DOI":"10.1097\/AOG.0b013e31829c3e64"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Langfelder, P., and Horvath, S. (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinform., 9.","DOI":"10.1186\/1471-2105-9-559"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/14789450.2020.1766975","article-title":"Network-based strategies in metabolomics data analysis and interpretation: From molecular networking to biological interpretation","volume":"17","author":"Alseekh","year":"2020","journal-title":"Expert Rev. Proteom."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, S., Park, Y., Duraisingham, S., Strobel, F.H., Khan, N., Soltow, Q.A., Jones, D.P., and Pulendran, B. (2013). Predicting network activity from high throughput metabolomics. PLoS Comput. Biol., 9.","DOI":"10.1371\/journal.pcbi.1003123"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"W71","DOI":"10.1093\/nar\/gkq329","article-title":"MSEA: A web-based tool to identify biologically meaningful patterns in quantitative metabolomic data","volume":"38","author":"Xia","year":"2010","journal-title":"Nucleic Acids Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"12698","DOI":"10.1073\/pnas.0914257107","article-title":"Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways","volume":"107","author":"Miller","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Langfelder, P., Luo, R., Oldham, M.C., and Horvath, S. (2011). Is my network module preserved and reproducible?. PLoS Comput. 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